Anomaly-based Intrusion Detection System Using Fuzzy Logic

TL;DR

This paper presents an anomaly-based intrusion detection system utilizing fuzzy logic to effectively identify DDOS attacks, achieving high accuracy and low false-positive rates on an open-source dataset.

Contribution

It introduces a fuzzy logic-based IDS specifically designed for DDOS detection, demonstrating improved detection performance with feature selection techniques.

Findings

Achieved 91.1% true-positive rate

False-positive rate of 0.006%

Effective detection using fuzzy inference and InfoGain features

Abstract

Recently, the Distributed Denial of Service (DDOS) attacks has been used for different aspects to denial the number of services for the end-users. Therefore, there is an urgent need to design an effective detection method against this type of attack. A fuzzy inference system offers the results in a more readable and understandable form. This paper introduces an anomaly-based Intrusion Detection (IDS) system using fuzzy logic. The fuzzy logic inference system implemented as a detection method for Distributed Denial of Service (DDOS) attacks. The suggested method was applied to an open-source DDOS dataset. Experimental results show that the anomaly-based Intrusion Detection system using fuzzy logic obtained the best result by utilizing the InfoGain features selection method besides the fuzzy inference system, the results were 91.1% for the true-positive rate and 0.006% for the false-positive rate.